Event Location: NSH 1507

Abstract: Sliding window scanning is the dominant paradigm in object recognition
research today. But while much success has been reported in detecting
several rectangular-shaped object classes (i.e. faces, cars,
pedestrians), results have been much less impressive for more general
types of objects. Several researchers have advocated the use of image
segmentation as a way to get a better spatial support for objects. In
this paper, our aim is to address this issue by studying the following
two questions: 1) how important is good spatial support for recognition?
2) can segmentation provide better spatial support for objects? To
answer the first, we compare recognition performance using ground-truth
segmentation vs. bounding boxes. To answer the second, we use the
multiple segmentation approach to evaluate how close can real segments
approach the ground-truth for real objects, and at what cost. Our
results demonstrate the importance of finding the right spatial support
for objects, and the feasibility of doing so without excessive
computational burden.